code: def __init__(self, task_id):
      task_name = list(tasks[task_id].keys())[0].split(".")[0]
      print(f"Preparing task {task_id} {task_name}")
      super().__init__(
          dataset_root=f"data/{task_name}",
          raw_dataset_dir=f"{task_name}_raw/",
          images_dir=f"{task_name}/{task_name}/imagesTr",
          labels_dir=f"{task_name}/{task_name}/labelsTr",
          phase_dir=f"{task_name}_phase0/",
          urls=tasks[task_id],
          valid_suffix=("nii.gz", "nii.gz"),
          filter_key=(None, None),
          uncompress_params={"format": "tar", "num_files": 1},
      )
  
      self.preprocess = {
          "images": [
              HUNorm,
              wrapped_partial(
                  resample, new_shape=[128, 128, 128], order=1
              ),
          ],
          "labels": [
              wrapped_partial(
                  resample, new_shape=[128, 128, 128], order=0
              ),
          ],
      }
  
file_path: PaddleSeg/contrib/MedicalSeg/tools/prepare_msd.py
imports: import os
  import os.path
  import sys
  from prepare import Prep
  from preprocess_utils import HUNorm, resample, parse_msd_basic_info, join_paths
  from medicalseg.utils import wrapped_partial
label_desc: Use image node_id 1 to create a new Prep_msd object from inherited base classes: Prep with example: obj = Prep_msd(task_id)